The present invention relates to a method of batching out foodstuff items in a weight controlled manner from an incoming flow of such items, in which flow the items occur with non-uniform weights, by effecting allocation of items to a plurality of receiver stations, the method comprising determining the weight of the individual items and reading the weights into a control unit, determining a preferential allocation of each item to a consequently selected receiver station based on
a) the total weight of items already located in the receiver station;
b) preset operational conditions such as batch target weight and item weight range, from which the item can be selected;
c) optionally, information as to expectable item weight distribution in said incoming flow of items; and effecting transfer of the item to the selected receiver station.
Typically, such an automatically controlled batching is carried out by means of a batching machine of the xe2x80x98graderxe2x80x99 type, comprising a weighing station for dynamically weighing the arriving items, and a following sorting-out conveyor passing along a row of receiver stations with associated, selectively operable diverter means for diverting the respective items into selected receiver stations, controlled by a control unit connected with the weighing station. The control unit or computer can be programmed with various operational conditions such as, of course, a desired target weight or minimum weight of the batches, an acceptable maximum overweight of each batch, and a specific item weight range for effectively selectable items.
The control unit may operate in a more or less sophisticated manner, spanning from a purely combinatory or simple accumulative merging of items based on the control unit allocating items up to a point, at which the control unit will, for each batch, call for a single item to conclude the batch within the preset weight limits, to higher developed methods of taking into account an expectable or actually detected weight distribution of the items in the supplied flow of items, whereby it is possible to control the batching process in such a manner that at the said xe2x80x9cbut onexe2x80x9d stage the relevant receiver station will call for an item which is likely to be present in the supplied flow with a high degree of possibility, e.g. an item exhibiting an average weight of the supplied items when these exhibit a normal distribution. By way of example, GB 2 116 732 and WO 96/08322 illustrate such methods and apparatuses.
Now, in connection with natural foodstuff items such as whole fishes or cut parts of chicken, it may well happen that during the operation there is a shift of goods from one supplier to another, which is likely to change the item weight distribution in the flow of items. This, in turn, may affect the ease with which the batcher is able to merge items to the required target weight.
It is already known that in connection with the handling of a specific order or task it is possible, by way of analysis or estimation, to determine the efficiency of the batching based on information as to the weight distribution of the items, primarily with the purpose of effecting current addition of items to the item flow, e.g. items with atypical weight, in order to facilitate batching in case of a xe2x80x9cdifficult taskxe2x80x9d. Typically, the target weight is desired to be a xe2x80x9cwholexe2x80x9d figure, e.g. 2 kg or 450 g, and all according to the weight distribution of the items it may be more or less easy to hit such figures. It has been found, however, that the said item addition technique is very difficult to administer.
With the present invention quite a different approach is made, viz. to effect estimation of a deviating target weight that will be better suited for a successful batching of the actual items, and then accept such an optimized target weight, leaving the xe2x80x9cwhole figurexe2x80x9d practice. Of course, the price of the batches should be adjusted correspondingly, but to the final customers it may not be critical if a package, correctly labelled, holds e.g. 438 g or 461 g instead of 450 g. On the other hand, however, this will generally increase both the production capacity of the supplier and the degree of utilisation of the raw material, so giving the supplier the economics of production which allows a lower product price. At the same time, there are ecological benefits from increased usagexe2x80x94practically 100%xe2x80x94of the raw material.
The ultimate aim of this method is to make use of the entire distribution of the arriving items for one or more batching tasks, but a result of the said estimation or analysis may, under circumstances, be that the batching efficiency could in fact be optimized by sorting out some specific items, e.g. certain percentages of items from different weight ranges, such that the sorted out items can be planned to be used otherwise (without amounting to waste), while the remaining items will be perfectly usable for the batching work.
This will imply a certain manipulation with the preset operational conditions, and when a high capacity analyzer is used, e.g. the control computer itself or an auxiliary computer, it will then be possible to check any relevant conditions in order to determine an optimum for all of the conditions. At the outset, the analyzer can be supplied with the same conditions as those read into the control computer, and while both computers receive the same input as to the weights of the arriving items, the analyzer can carry out trial changes of the relevant conditions and by an evaluation function or an iterative process come up with different simulated results of such changes, which are then compared with the efficiency of the control unit""s handling of the task. Some results will be poorer, but one or more may be better. If a better result is achieved (higher efficiency, less waste, less overweight, etc.), which shows a tendency to stabilize, a corresponding adjustment can be made, either manually or automatically, to the control unit, after which the simulator again begins to search for further possibilities of optimization.
The setting up of the operational conditions can be more or less detailed, and so the number of adjustable parameters will vary correspondingly. A basic parameter will be a minimum batch weight, and already at this point the simulator may recommend or effect an adjustment that will reduce the average overweight in connection with the conclusion of the respective batches by the allocation of the last item that will increase the batch weight to beyond the required minimum.
A further relevant parameter can be an upper limit for the overweight of each batch. This, in turn, may influence other parameters such as the number of receiving stations used for the particular batching job or task in cases where one or more other jobs are being handled simultaneously, thus also requiring a certain number of the available receiving stations. It will be readily understood that a requirement with respect to the addition of a last item for bringing the batch weight up to a limited overweight will call for relatively many receiver stations each waiting for an item to fulfill just this condition, in particular if the xe2x80x9climited overweightxe2x80x9d is further defined as xe2x80x9cthe least possible overweightxe2x80x9d.
It is relevant at this point to mention that it may be beneficial to use a further control function, which can be designated xe2x80x9cforced portion conclusionxe2x80x9d, referring to a situation in which a built-up partial batch lacking only a single item to reach the target weight does not, over a longer period of time, receive such an item. It reduces the efficiency of the apparatus if one or more receiver stations thus stand waiting passively for longer periods, in which case it is better and more economical to forcibly conclude such batches with an over-overweight item in order to then make the receiver station operative again.
Still further relevant control parameters will be the minimum and the maximum weight limits, within which the items for a given job or task are to be located, this being a typical wholesaler demand. However, if the supplier can demonstrate, based on the results of the simulator, that a better result is achievable by some deviation from these strict limits, then the customer may well renounce the strict requirements, still given that the associated changes will not affect the end user to any appreciable degree.